Cloud workflow scheduling is significantly challenging due to not only the large scale of workflow but also the elasticity and heterogeneity of cloud resources. Moreover, the pricing model of clouds makes the execution time and execution cost two critical issues in the scheduling. This paper models the cloud workflow scheduling as a multiobjective optimization problem that optimizes both execution time and execution cost. A novel multiobjective ant colony system based on a co-evolutionary multiple populations for multiple objectives framework is proposed, which adopts two colonies to deal with these two objectives, respectively. Moreover, the proposed approach incorporates with the following three novel designs to efficiently deal with the multiobjective challenges: 1) a new pheromone update rule based on a set of nondominated solutions from a global archive to guide each colony to search its optimization objective sufficiently; 2) a complementary heuristic strategy to avoid a colony only focusing on its corresponding single optimization objective, cooperating with the pheromone update rule to balance the search of both objectives; and 3) an elite study strategy to improve the solution quality of the global archive to help further approach the global Pareto front. Experimental simulations are conducted on five types of real-world scientific workflows and consider the properties of Amazon EC2 cloud platform. The experimental results show that the proposed algorithm performs better than both some state-of-the-art multiobjective optimization approaches and the constrained optimization approaches.

中文翻译：

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多目标云工作流调度：多群体蚁群系统方法


由于工作流规模巨大，而且云资源的弹性和异构性，云工作流调度面临着巨大的挑战。此外，云的定价模型使得执行时间和执行成本成为调度中的两个关键问题。本文将云工作流调度建模为一个多目标优化问题，优化执行时间和执行成本。提出了一种基于多目标协同进化多群体框架的新型多目标蚁群系统，采用两个蚁群分别处理这两个目标。此外，所提出的方法结合了以下三种新颖的设计，以有效地应对多目标挑战：1）基于全局档案中的一组非支配解决方案的新信息素更新规则，以指导每个群体充分搜索其优化目标； 2）互补启发式策略，避免群体只关注其对应的单一优化目标，配合信息素更新规则平衡两个目标的搜索； 3）精英研究策略，提高全球档案的解决方案质量，帮助进一步接近全球帕累托前沿。对五种类型的现实科学工作流程进行了实验模拟，并考虑了 Amazon EC2 云平台的属性。实验结果表明，所提出的算法比一些最先进的多目标优化方法和约束优化方法表现更好。